Ab Initio bulk free energy surface of proper ferroelectrics

ORAL

Abstract

The bulk free energy surface (FES) is a fundamental thermodynamic quantity that describes the mesoscopic/macroscopic properties of ferroic materials. Deriving the FES directly from first-principles calculations poses a formidable challenge.

We report a systematic and accurate approach [1] for deriving the bulk FES as a function of temperature, polarization vector, and strain tensor, from the first-principles density functional theory (DFT) of proper ferroelectrics. The core of our approach is the well-tempered metadynamics (WT-MetaD) algorithm that extracts the polarization dependence of the FES from all-atom molecular dynamics simulations without an a priori ansatz. The rest of the FES is derived from the WT-MetaD trajectories that span the relevant phase space. We demonstrate our approach in the case of lead titanate. The errors across the phase transition, due to DFT numerics, all-atom molecular dynamics, and free energy evaluation by enhanced sampling, can be systematically controlled and are of the order of 1 meV/atom. The accuracy of the resulting ab initio FES is only limited by the adopted functional approximation of DFT.

[1] Pinchen Xie, Yixiao Chen, Xinyu Xu, Zhi Yao, and Roberto Car. "Ab Initio bulk free energy surface of proper ferroelectrics." arXiv preprint arXiv:2205.11839v2 (2025).

*P.X., Y.C., W. E, R.C. were supported by the Computational Chemical Sciences Center: Chemistry in Solution and at Interfaces (CSI) funded by U.S. Department of Energy (DOE) Award DE-SC0019394. P.X. and X.X. were supported by DOE Advanced Scientific Computing Research (ASCR) Applied Mathematics program under Contract No. DE-AC02-05CH11231. Calculations were performed on the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. Calculations were also performed using the Princeton Research Computing resources at Princeton University, which is a consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology's Research Computing.

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Presenters

  • Xinyu Xu

    • Lawrence Berkeley National Laboratory

Authors

  • Xinyu Xu

    • Lawrence Berkeley National Laboratory

  • Pinchen Xie

    • Lawrence Berkeley National Lab

  • Yixiao Chen

    • Princeton University

  • Zhi (Jackie) Yao

    • Lawrence Berkeley National Laboratory

  • Weinan E

    • Princeton University

  • Roberto Car

    • Princeton University